Going Back to the Basics?

With data science languages, sometimes learning the basics can be the hardest part. The QAC offers several .25 credit classes that introduce students to the necessities of different languages, but even fitting all the necessary information into a half a semester can be difficult. This past quarter, Professor Pavel Oleinikov utilized a website called DataCamp to help his students get comfortable with the basics of Python. DataCamp is an online collection of data science lessons that teaches users through videos and repetitive exercises. The website has an in-browser code box that allows users to code right on the website without having to download any software. Each lesson takes roughly 30 minutes to 1 hour to complete, making it a convenient way to nail down a specific skill.

Students in Pavel’s Working with Python class really enjoyed being assigned DataCamp lessons as homework. “We only have 3 hours, and that may seem long but that’s not a lot of time considering the concepts that we’re learning,” said Anthony Price. These .25 credit classes move quickly, and so there isn’t much time to backtrack if students are lost. And students can always Google around for answers, but sometimes the vast amount of material returned can be overwhelming. This is why it is important to have resources in place so that students don’t give up before they get comfortable.


When Anthony was unsure of how to do something, he turned to a DataCamp lesson. Each lesson focuses on a specific function or package, and first explains how to use said function through a video before asking the user to complete various tasks. The tasks often involve completing a partially written line of code and then printing out the results. A user can submit their code to see if it is correct, and if so they can move on, and if not they can keep trying or ask for a hint. “I really enjoyed how it would say ‘Oh, you made a mistake. Try something else.’ So it was kind of like one big puzzle,” said Anthony. “But it’s also not punitive,” added Alex Richwine, referring to the fact that a user can attempt a problem as many times as they please. “So I think a key thing is that it lets you work at your own pace.”

Rocco Davino was one of the few students in the class who had used DataCamp before. He was participating in a competition hosted by the website Kaggle, and there was an advertisement telling participants to go to DataCamp if they wanted to learn how to do the necessary coding. This sort of partnership shows how DataCamp has been climbing the ranks in online coding sites. But why is it working so well? “I think it is unique in that it focuses on data analysis,” said Rocco. “I’d say they have more power if they stick to their niche. There are already a ton of websites that teach just programming.”

And this is not the only way in which DataCamp might be better off to sticking with what it already has. Because the true magic of DataCamp is not its data-focus, but it’s teaching method. Datacamp’s combination of videos and repetitive lessons creates a goal of reinforcement that is both effective but not over the top. It creates a sort of individualized dynamic in which the student can take control of the learning process. Anthony added, “I think it was really unique that we were able to code and manipulate data right on the website and see what we were doing. So it wasn’t like ‘Find the Solution.’ It was like, ‘Explore. Why do you think this is the answer?’”

And, most importantly, this method lends itself best to those pesky basics. This puts DataCamp in the perfect position to be an invaluable tool for courses like the QAC’s introductions to languages. And it can be valuable in other departments that desire to incorporate a little bit of data analysis into their curriculum but don’t have time to teach the skills. Alex has experienced this in his ECON300 class. “It’s kind of tough working on the final project without a mastery of the programing,” he explained. “So I think it would be really good if we applied something like DataCamp to ECON classes.”

Students have not yet attempted the advanced chapters of DataCamp, but as they expand it is possible that they will be used in QAC classes. And it will be interesting to see how DataCamp adjusts their method for higher level concepts. Alex wonders if it will work as well, mentioning that “I do feel just on intuition that for really advanced python skills you do need either individual instruction or just really focused devotion to this advanced concept. There’s just some stuff where you can’t do an exercise and get it down.” It is true that data analysis languages do not exist on an even learning curve. It is less of a ladder, where all these different skills are building off one another, and more of a branching tree. So perhaps that will be a hurdle to overcome with future DataCamp usage. “Or it could keep you on your toes,” Anthony countered. “You know, you may get lazy, and then you can go back to DataCamp to get your brain moving.”